Overview

Dataset statistics

Number of variables20
Number of observations1079565
Missing cells0
Missing cells (%)0.0%
Duplicate rows160123
Duplicate rows (%)14.8%
Total size in memory164.7 MiB
Average record size in memory160.0 B

Variable types

Numeric20

Alerts

Dataset has 160123 (14.8%) duplicate rowsDuplicates
FUEL_USED_2 is highly correlated with FUEL_USED_3 and 5 other fieldsHigh correlation
FUEL_USED_3 is highly correlated with FUEL_USED_2 and 5 other fieldsHigh correlation
FUEL_USED_4 is highly correlated with FUEL_USED_2 and 5 other fieldsHigh correlation
FW_GEO_ALTITUDE is highly correlated with FUEL_USED_2 and 6 other fieldsHigh correlation
VALUE_FOB is highly correlated with VALUE_FUEL_QTY_CT and 3 other fieldsHigh correlation
VALUE_FUEL_QTY_CT is highly correlated with VALUE_FOB and 1 other fieldsHigh correlation
VALUE_FUEL_QTY_FT1 is highly correlated with VALUE_FUEL_QTY_FT2 and 2 other fieldsHigh correlation
VALUE_FUEL_QTY_FT2 is highly correlated with VALUE_FUEL_QTY_FT1 and 2 other fieldsHigh correlation
VALUE_FUEL_QTY_FT3 is highly correlated with VALUE_FUEL_QTY_FT1 and 2 other fieldsHigh correlation
VALUE_FUEL_QTY_FT4 is highly correlated with VALUE_FUEL_QTY_FT1 and 2 other fieldsHigh correlation
VALUE_FUEL_QTY_LXT is highly correlated with VALUE_FOB and 2 other fieldsHigh correlation
VALUE_FUEL_QTY_RXT is highly correlated with VALUE_FOB and 2 other fieldsHigh correlation
FLIGHT_PHASE_COUNT is highly correlated with FUEL_USED_2 and 5 other fieldsHigh correlation
FUEL_USED_1 is highly correlated with FUEL_USED_2 and 5 other fieldsHigh correlation
TOTAL_FUEL_USED is highly correlated with FUEL_USED_2 and 5 other fieldsHigh correlation
TOTAL_FOB_BY_QTY is highly correlated with VALUE_FOB and 3 other fieldsHigh correlation
ALTITUDE_DIFF is highly correlated with FW_GEO_ALTITUDEHigh correlation
FUEL_USED_2 is highly correlated with FUEL_USED_3 and 5 other fieldsHigh correlation
FUEL_USED_3 is highly correlated with FUEL_USED_2 and 5 other fieldsHigh correlation
FUEL_USED_4 is highly correlated with FUEL_USED_2 and 3 other fieldsHigh correlation
FW_GEO_ALTITUDE is highly correlated with FUEL_USED_2 and 3 other fieldsHigh correlation
VALUE_FOB is highly correlated with VALUE_FUEL_QTY_CT and 3 other fieldsHigh correlation
VALUE_FUEL_QTY_CT is highly correlated with VALUE_FOB and 1 other fieldsHigh correlation
VALUE_FUEL_QTY_FT1 is highly correlated with VALUE_FUEL_QTY_FT2 and 2 other fieldsHigh correlation
VALUE_FUEL_QTY_FT2 is highly correlated with VALUE_FUEL_QTY_FT1 and 2 other fieldsHigh correlation
VALUE_FUEL_QTY_FT3 is highly correlated with VALUE_FUEL_QTY_FT1 and 2 other fieldsHigh correlation
VALUE_FUEL_QTY_FT4 is highly correlated with VALUE_FUEL_QTY_FT1 and 2 other fieldsHigh correlation
VALUE_FUEL_QTY_LXT is highly correlated with VALUE_FOB and 2 other fieldsHigh correlation
VALUE_FUEL_QTY_RXT is highly correlated with VALUE_FOB and 2 other fieldsHigh correlation
FLIGHT_PHASE_COUNT is highly correlated with FUEL_USED_2 and 4 other fieldsHigh correlation
FUEL_USED_1 is highly correlated with FUEL_USED_2 and 4 other fieldsHigh correlation
TOTAL_FUEL_USED is highly correlated with FUEL_USED_2 and 5 other fieldsHigh correlation
TOTAL_FOB_BY_QTY is highly correlated with VALUE_FOB and 3 other fieldsHigh correlation
FUEL_USED_2 is highly correlated with FUEL_USED_3 and 5 other fieldsHigh correlation
FUEL_USED_3 is highly correlated with FUEL_USED_2 and 5 other fieldsHigh correlation
FUEL_USED_4 is highly correlated with FUEL_USED_2 and 4 other fieldsHigh correlation
FW_GEO_ALTITUDE is highly correlated with FUEL_USED_2 and 2 other fieldsHigh correlation
VALUE_FOB is highly correlated with VALUE_FUEL_QTY_LXT and 2 other fieldsHigh correlation
VALUE_FUEL_QTY_LXT is highly correlated with VALUE_FOB and 2 other fieldsHigh correlation
VALUE_FUEL_QTY_RXT is highly correlated with VALUE_FOB and 2 other fieldsHigh correlation
FLIGHT_PHASE_COUNT is highly correlated with FUEL_USED_2 and 5 other fieldsHigh correlation
FUEL_USED_1 is highly correlated with FUEL_USED_2 and 4 other fieldsHigh correlation
TOTAL_FUEL_USED is highly correlated with FUEL_USED_2 and 4 other fieldsHigh correlation
TOTAL_FOB_BY_QTY is highly correlated with VALUE_FOB and 2 other fieldsHigh correlation
FUEL_USED_2 is highly correlated with FUEL_USED_3 and 6 other fieldsHigh correlation
FUEL_USED_3 is highly correlated with FUEL_USED_2 and 5 other fieldsHigh correlation
FUEL_USED_4 is highly correlated with FUEL_USED_2 and 5 other fieldsHigh correlation
FW_GEO_ALTITUDE is highly correlated with FUEL_USED_2 and 6 other fieldsHigh correlation
VALUE_FOB is highly correlated with VALUE_FUEL_QTY_CT and 7 other fieldsHigh correlation
VALUE_FUEL_QTY_CT is highly correlated with VALUE_FOB and 3 other fieldsHigh correlation
VALUE_FUEL_QTY_FT1 is highly correlated with VALUE_FOB and 5 other fieldsHigh correlation
VALUE_FUEL_QTY_FT2 is highly correlated with VALUE_FOB and 6 other fieldsHigh correlation
VALUE_FUEL_QTY_FT3 is highly correlated with VALUE_FOB and 5 other fieldsHigh correlation
VALUE_FUEL_QTY_FT4 is highly correlated with VALUE_FOB and 5 other fieldsHigh correlation
VALUE_FUEL_QTY_LXT is highly correlated with FUEL_USED_2 and 5 other fieldsHigh correlation
VALUE_FUEL_QTY_RXT is highly correlated with VALUE_FOB and 3 other fieldsHigh correlation
FLIGHT_PHASE_COUNT is highly correlated with FUEL_USED_2 and 6 other fieldsHigh correlation
FUEL_USED_1 is highly correlated with FUEL_USED_2 and 5 other fieldsHigh correlation
TOTAL_FUEL_USED is highly correlated with FUEL_USED_2 and 5 other fieldsHigh correlation
TOTAL_FOB_BY_QTY is highly correlated with VALUE_FOB and 7 other fieldsHigh correlation
ALTITUDE_DIFF is highly correlated with FW_GEO_ALTITUDE and 1 other fieldsHigh correlation
VALUE_FOB_MISSING is highly correlated with VALUE_FUEL_QTY_FT1 and 3 other fieldsHigh correlation

Reproduction

Analysis started2023-06-16 09:25:29.327252
Analysis finished2023-06-16 09:30:13.784337
Duration4 minutes and 44.46 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

FUEL_USED_2
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct552992
Distinct (%)51.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-3.964747707 × 10-16
Minimum-0.8026063562
Maximum3.694108331
Zeros0
Zeros (%)0.0%
Negative700075
Negative (%)64.8%
Memory size8.2 MiB
2023-06-16T11:30:13.940535image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-0.8026063562
5-th percentile-0.7991127504
Q1-0.7873625096
median-0.4212396167
Q30.526600396
95-th percentile2.252979145
Maximum3.694108331
Range4.496714687
Interquartile range (IQR)1.313962906

Descriptive statistics

Standard deviation1.000000463
Coefficient of variation (CV)-2.522229753 × 1015
Kurtosis0.9685766375
Mean-3.964747707 × 10-16
Median Absolute Deviation (MAD)0.3748046642
Skewness1.357947725
Sum-4.137774567 × 10-10
Variance1.000000926
MonotonicityNot monotonic
2023-06-16T11:30:14.065671image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.73008475895500
 
0.5%
-0.77766841394144
 
0.4%
-0.80260635623725
 
0.3%
-0.71759754053088
 
0.3%
2.8812353613036
 
0.3%
-0.7631167922284
 
0.2%
-0.74785616142216
 
0.2%
-0.73908165441996
 
0.2%
-0.76136559561964
 
0.2%
-0.079718360041538
 
0.1%
Other values (552982)1050074
97.3%
ValueCountFrequency (%)
-0.80260635623725
0.3%
-0.80260586134
 
< 0.1%
-0.80260557591
 
< 0.1%
-0.80260557061
 
< 0.1%
-0.80260556051
 
< 0.1%
-0.80260555991
 
< 0.1%
-0.80260554811
 
< 0.1%
-0.80260553961
 
< 0.1%
-0.80260553821
 
< 0.1%
-0.80260553674
 
< 0.1%
ValueCountFrequency (%)
3.6941083311
< 0.1%
3.6939834681
< 0.1%
3.6938708531
< 0.1%
3.6937555491
< 0.1%
3.693639051
< 0.1%
3.6935049271
< 0.1%
3.6933791681
< 0.1%
3.6932283181
< 0.1%
3.6931055461
< 0.1%
3.6929412531
< 0.1%

FUEL_USED_3
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct552684
Distinct (%)51.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.062093551 × 10-16
Minimum-0.8046376361
Maximum3.65322153
Zeros0
Zeros (%)0.0%
Negative697910
Negative (%)64.6%
Memory size8.2 MiB
2023-06-16T11:30:14.192724image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-0.8046376361
5-th percentile-0.8033950072
Q1-0.7936569279
median-0.4206223691
Q30.5260913255
95-th percentile2.247789453
Maximum3.65322153
Range4.457859166
Interquartile range (IQR)1.319748253

Descriptive statistics

Standard deviation1.000000463
Coefficient of variation (CV)3.265741058 × 1015
Kurtosis0.9071490501
Mean3.062093551 × 10-16
Median Absolute Deviation (MAD)0.3800603602
Skewness1.341353421
Sum3.017959216 × 10-10
Variance1.000000926
MonotonicityNot monotonic
2023-06-16T11:30:14.312268image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.80463763616194
 
0.6%
-0.74757639243088
 
0.3%
2.9087314063036
 
0.3%
-0.27336871652680
 
0.2%
-0.75484330362492
 
0.2%
-0.78668779822180
 
0.2%
-0.77644510062088
 
0.2%
-0.74653318832040
 
0.2%
0.027017863651704
 
0.2%
-0.15643020751684
 
0.2%
Other values (552674)1052379
97.5%
ValueCountFrequency (%)
-0.80463763616194
0.6%
-0.8046370851
 
< 0.1%
-0.80463705191
 
< 0.1%
-0.80463685541
 
< 0.1%
-0.80463684321
 
< 0.1%
-0.8046368341
 
< 0.1%
-0.80463683091
 
< 0.1%
-0.80463682911
 
< 0.1%
-0.80463682841
 
< 0.1%
-0.80463682022
 
< 0.1%
ValueCountFrequency (%)
3.653221531
< 0.1%
3.6531108181
< 0.1%
3.6529853061
< 0.1%
3.6528707471
< 0.1%
3.6527550031
< 0.1%
3.6526365961
< 0.1%
3.6524965781
< 0.1%
3.6523639621
< 0.1%
3.6522242411
< 0.1%
3.6520794871
< 0.1%

FUEL_USED_4
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct504404
Distinct (%)46.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.120175743 × 10-16
Minimum-0.819411096
Maximum3.471880078
Zeros0
Zeros (%)0.0%
Negative696864
Negative (%)64.6%
Memory size8.2 MiB
2023-06-16T11:30:14.536274image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-0.819411096
5-th percentile-0.8194099062
Q1-0.8113452708
median-0.428022239
Q30.5177289328
95-th percentile2.259541765
Maximum3.471880078
Range4.291291175
Interquartile range (IQR)1.329074204

Descriptive statistics

Standard deviation1.000000463
Coefficient of variation (CV)2.42708206 × 1015
Kurtosis0.7763227704
Mean4.120175743 × 10-16
Median Absolute Deviation (MAD)0.3896517324
Skewness1.301186039
Sum5.182982932 × 10-10
Variance1.000000926
MonotonicityNot monotonic
2023-06-16T11:30:14.648619image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.81941109635481
 
3.3%
-0.79388210265068
 
0.5%
-0.80660563853560
 
0.3%
-0.19616332513152
 
0.3%
2.7503235353036
 
0.3%
-0.80954141431816
 
0.2%
-0.31520563381804
 
0.2%
-0.015178945831704
 
0.2%
-0.755209991590
 
0.1%
-0.12977820581538
 
0.1%
Other values (504394)1020816
94.6%
ValueCountFrequency (%)
-0.81941109635481
3.3%
-0.81941106084
 
< 0.1%
-0.81941095851
 
< 0.1%
-0.81941093284
 
< 0.1%
-0.81941089861
 
< 0.1%
-0.81941086951
 
< 0.1%
-0.81941079251
 
< 0.1%
-0.81941054481
 
< 0.1%
-0.81941052791
 
< 0.1%
-0.81941052611
 
< 0.1%
ValueCountFrequency (%)
3.4718800781
< 0.1%
3.4717593561
< 0.1%
3.4716372041
< 0.1%
3.4715256361
< 0.1%
3.4714140681
< 0.1%
3.4712990671
< 0.1%
3.4711631831
< 0.1%
3.4710344511
< 0.1%
3.4708819751
< 0.1%
3.4707589641
< 0.1%

FW_GEO_ALTITUDE
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct41598
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.271943888 × 10-17
Minimum-1.033568821
Maximum1.438509613
Zeros0
Zeros (%)0.0%
Negative577371
Negative (%)53.5%
Memory size8.2 MiB
2023-06-16T11:30:14.764110image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1.033568821
5-th percentile-1.001977845
Q1-0.9706574556
median-0.7519559019
Q31.09033241
95-th percentile1.232187393
Maximum1.438509613
Range2.472078434
Interquartile range (IQR)2.060989866

Descriptive statistics

Standard deviation1.000000463
Coefficient of variation (CV)1.208906246 × 1016
Kurtosis-1.905648428
Mean8.271943888 × 10-17
Median Absolute Deviation (MAD)0.2578013056
Skewness0.1618647395
Sum9.003997548 × 10-11
Variance1.000000926
MonotonicityNot monotonic
2023-06-16T11:30:14.892245image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0964206076178
 
0.6%
1.0964882546004
 
0.6%
1.096352965452
 
0.5%
1.09655595190
 
0.5%
1.0962853144809
 
0.4%
1.0288416244307
 
0.4%
1.0287739774290
 
0.4%
1.0966235474238
 
0.4%
1.0962176673983
 
0.4%
1.0287063313879
 
0.4%
Other values (41588)1031235
95.5%
ValueCountFrequency (%)
-1.0335688215
 
< 0.1%
-1.03350117549
< 0.1%
-1.0334335287
 
< 0.1%
-1.03336588118
 
< 0.1%
-1.03329823516
 
< 0.1%
-1.0332305885
 
< 0.1%
-1.0290364979
 
< 0.1%
-1.0289688517
 
< 0.1%
-1.0289012046
 
< 0.1%
-1.02883355713
 
< 0.1%
ValueCountFrequency (%)
1.4385096131
 
< 0.1%
1.438374321
 
< 0.1%
1.4382390271
 
< 0.1%
1.438171382
 
< 0.1%
1.4381037333
< 0.1%
1.437968443
< 0.1%
1.4379007944
< 0.1%
1.4378331472
 
< 0.1%
1.43776557
< 0.1%
1.4376978544
< 0.1%

VALUE_FOB
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct36434
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.613318655 × 10-16
Minimum-2.485316143
Maximum2.365446498
Zeros0
Zeros (%)0.0%
Negative537593
Negative (%)49.8%
Memory size8.2 MiB
2023-06-16T11:30:15.010300image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2.485316143
5-th percentile-1.592370796
Q1-0.7253721243
median0.006703834261
Q30.7786490748
95-th percentile1.67931514
Maximum2.365446498
Range4.850762641
Interquartile range (IQR)1.504021199

Descriptive statistics

Standard deviation1.000000463
Coefficient of variation (CV)-6.198406372 × 1015
Kurtosis-0.7503029523
Mean-1.613318655 × 10-16
Median Absolute Deviation (MAD)0.7597946023
Skewness0.02385944118
Sum-1.782609615 × 10-10
Variance1.000000926
MonotonicityNot monotonic
2023-06-16T11:30:15.122951image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.4698290122279
 
< 0.1%
-0.4700821505249
 
< 0.1%
-0.4771700229249
 
< 0.1%
-0.4736260867240
 
< 0.1%
0.149094127235
 
< 0.1%
-0.4699555814234
 
< 0.1%
0.7863697929229
 
< 0.1%
0.7829524259223
 
< 0.1%
-0.4702087197223
 
< 0.1%
-1.513644785222
 
< 0.1%
Other values (36424)1077182
99.8%
ValueCountFrequency (%)
-2.4853161431
< 0.1%
-2.4849364361
< 0.1%
-2.4844301591
< 0.1%
-2.4837973131
< 0.1%
-2.4831644681
< 0.1%
-2.4820253451
< 0.1%
-2.4815190691
< 0.1%
-2.4807596541
< 0.1%
-2.4800002391
< 0.1%
-2.4794939621
< 0.1%
ValueCountFrequency (%)
2.3654464981
< 0.1%
2.3653199281
< 0.1%
2.3649402211
< 0.1%
2.3645605141
< 0.1%
2.3644339441
< 0.1%
2.3641808062
< 0.1%
2.3636745291
< 0.1%
2.363547961
< 0.1%
2.3631682531
< 0.1%
2.3629151151
< 0.1%

VALUE_FUEL_QTY_CT
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct7870
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.482736727 × 10-16
Minimum-0.410996508
Maximum5.510787062
Zeros0
Zeros (%)0.0%
Negative883802
Negative (%)81.9%
Memory size8.2 MiB
2023-06-16T11:30:15.234421image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-0.410996508
5-th percentile-0.410996508
Q1-0.410996508
median-0.410996508
Q3-0.3730189679
95-th percentile2.44274436
Maximum5.510787062
Range5.92178357
Interquartile range (IQR)0.03797754007

Descriptive statistics

Standard deviation1.000000463
Coefficient of variation (CV)-6.744288751 × 1015
Kurtosis7.242811535
Mean-1.482736727 × 10-16
Median Absolute Deviation (MAD)0
Skewness2.761393894
Sum8.169465104 × 10-11
Variance1.000000926
MonotonicityNot monotonic
2023-06-16T11:30:15.346821image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.410996508701009
64.9%
-0.410318337615792
 
1.5%
-0.40964016739880
 
0.9%
-0.40896199697188
 
0.7%
-0.40828382665647
 
0.5%
-0.17974041584807
 
0.4%
-0.40760565624596
 
0.4%
-0.40692748583938
 
0.4%
-0.40624931553490
 
0.3%
-0.40557114513227
 
0.3%
Other values (7860)319991
29.6%
ValueCountFrequency (%)
-0.410996508701009
64.9%
-0.410318337615792
 
1.5%
-0.40964016739880
 
0.9%
-0.40896199697188
 
0.7%
-0.40828382665647
 
0.5%
-0.40760565624596
 
0.4%
-0.40692748583938
 
0.4%
-0.40624931553490
 
0.3%
-0.40557114513227
 
0.3%
-0.40489297482800
 
0.3%
ValueCountFrequency (%)
5.5107870621
< 0.1%
5.507396211
< 0.1%
5.5040053581
< 0.1%
5.5006145071
< 0.1%
5.4972236551
< 0.1%
5.4938328031
< 0.1%
5.4904419511
< 0.1%
5.4863729291
< 0.1%
5.4836602481
< 0.1%
5.4802693961
< 0.1%

VALUE_FUEL_QTY_FT1
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1354
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6.28478181 × 10-16
Minimum-4.362634003
Maximum3.089633468
Zeros0
Zeros (%)0.0%
Negative385411
Negative (%)35.7%
Memory size8.2 MiB
2023-06-16T11:30:15.461742image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-4.362634003
5-th percentile-2.015033261
Q1-0.2461434001
median0.3161888706
Q30.6983564333
95-th percentile1.075064459
Maximum3.089633468
Range7.452267471
Interquartile range (IQR)0.9444998333

Descriptive statistics

Standard deviation1.000000463
Coefficient of variation (CV)-1.591145872 × 1015
Kurtosis0.336845458
Mean-6.28478181 × 10-16
Median Absolute Deviation (MAD)0.4531415385
Skewness-0.9813412967
Sum-6.367937289 × 10-10
Variance1.000000926
MonotonicityNot monotonic
2023-06-16T11:30:15.573063image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.5673512596438
 
0.6%
0.58916570115696
 
0.5%
0.59462523775449
 
0.5%
0.58370616455430
 
0.5%
0.54548940825429
 
0.5%
0.54002987165371
 
0.5%
0.51819172525361
 
0.5%
0.61646338415159
 
0.5%
0.60008477435147
 
0.5%
0.52365126185138
 
0.5%
Other values (1344)1024947
94.9%
ValueCountFrequency (%)
-4.3626340031
 
< 0.1%
-4.3571744661
 
< 0.1%
-4.351714931
 
< 0.1%
-4.3407958571
 
< 0.1%
-4.3244172473
 
< 0.1%
-4.3189577110
< 0.1%
-4.31349817413
< 0.1%
-4.30803863716
< 0.1%
-4.302579115
< 0.1%
-4.29711956412
< 0.1%
ValueCountFrequency (%)
3.0896334686
 
< 0.1%
3.0841739317
 
< 0.1%
3.0787143954
 
< 0.1%
3.0732548584
 
< 0.1%
3.0677953213
 
< 0.1%
3.0623357851
 
< 0.1%
3.05687624818
< 0.1%
3.0459571753
 
< 0.1%
3.0404976382
 
< 0.1%
3.0186594921
 
< 0.1%

VALUE_FUEL_QTY_FT2
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2021
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-8.896420364 × 10-16
Minimum-6.24667925
Maximum2.643068803
Zeros0
Zeros (%)0.0%
Negative296678
Negative (%)27.5%
Memory size8.2 MiB
2023-06-16T11:30:15.693876image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-6.24667925
5-th percentile-1.894621619
Q1-0.2033006432
median0.3907242848
Q30.6547353639
95-th percentile0.9641233472
Maximum2.643068803
Range8.889748053
Interquartile range (IQR)0.858036007

Descriptive statistics

Standard deviation1.000000463
Coefficient of variation (CV)-1.124048125 × 1015
Kurtosis2.0542133
Mean-8.896420364 × 10-16
Median Absolute Deviation (MAD)0.3382641951
Skewness-1.421899231
Sum-9.294423009 × 10-10
Variance1.000000926
MonotonicityNot monotonic
2023-06-16T11:30:15.804653image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5846074217398
 
0.7%
0.6217339797237
 
0.7%
0.56810672856273
 
0.6%
0.60523328656221
 
0.6%
0.62585915216178
 
0.6%
0.61760880596127
 
0.6%
0.60935845966033
 
0.6%
0.58048224796001
 
0.6%
0.60110811345999
 
0.6%
0.61348363285908
 
0.5%
Other values (2011)1016190
94.1%
ValueCountFrequency (%)
-6.246679251
 
< 0.1%
-6.2425540771
 
< 0.1%
-6.2384289042
 
< 0.1%
-6.2343037311
 
< 0.1%
-6.2260533852
 
< 0.1%
-6.2219282122
 
< 0.1%
-6.2178030384
< 0.1%
-6.2136778653
< 0.1%
-6.2095526925
< 0.1%
-6.1971771732
 
< 0.1%
ValueCountFrequency (%)
2.6430688039
< 0.1%
2.6389436314
< 0.1%
2.63481845710
< 0.1%
2.6306932849
< 0.1%
2.6265681114
 
< 0.1%
2.6224429382
 
< 0.1%
2.6059422451
 
< 0.1%
2.5811912078
< 0.1%
2.57706603314
< 0.1%
2.572940866
< 0.1%

VALUE_FUEL_QTY_FT3
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2035
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.717798678 × 10-16
Minimum-6.245654315
Maximum2.231284146
Zeros0
Zeros (%)0.0%
Negative305045
Negative (%)28.3%
Memory size8.2 MiB
2023-06-16T11:30:15.925149image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-6.245654315
5-th percentile-1.86511349
Q1-0.7009157034
median0.3409426548
Q30.7119073733
95-th percentile1.015782728
Maximum2.231284146
Range8.476938461
Interquartile range (IQR)1.412823077

Descriptive statistics

Standard deviation1.000000463
Coefficient of variation (CV)2.119633607 × 1015
Kurtosis2.247613161
Mean4.717798678 × 10-16
Median Absolute Deviation (MAD)0.4183219166
Skewness-1.372065317
Sum5.07027309 × 10-10
Variance1.000000926
MonotonicityNot monotonic
2023-06-16T11:30:16.121832image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0276220274711
 
0.4%
1.0355148944699
 
0.4%
1.0236755944479
 
0.4%
1.0315684614472
 
0.4%
1.0197291614299
 
0.4%
0.41592488524217
 
0.4%
1.0157827284183
 
0.4%
1.0118362954156
 
0.4%
1.0078898614156
 
0.4%
1.0394613274115
 
0.4%
Other values (2025)1036078
96.0%
ValueCountFrequency (%)
-6.2456543151
< 0.1%
-6.2219757161
< 0.1%
-6.2180292831
< 0.1%
-6.1943506842
< 0.1%
-6.190404251
< 0.1%
-6.1746185181
< 0.1%
-6.1706720841
< 0.1%
-6.1667256511
< 0.1%
-6.1627792182
< 0.1%
-6.1588327851
< 0.1%
ValueCountFrequency (%)
2.2312841465
< 0.1%
2.2273377138
< 0.1%
2.2233912796
< 0.1%
2.2194448467
< 0.1%
2.2154984135
< 0.1%
2.211551986
< 0.1%
2.2076055474
 
< 0.1%
2.20365911410
< 0.1%
2.199712684
 
< 0.1%
2.1957662474
 
< 0.1%

VALUE_FUEL_QTY_FT4
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1392
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.962941236 × 10-16
Minimum-4.446055261
Maximum3.080389247
Zeros0
Zeros (%)0.0%
Negative395609
Negative (%)36.6%
Memory size8.2 MiB
2023-06-16T11:30:16.235929image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-4.446055261
5-th percentile-1.947803856
Q1-0.284063491
median0.2916434925
Q30.6877721508
95-th percentile1.03636537
Maximum3.080389247
Range7.526444508
Interquartile range (IQR)0.9718356418

Descriptive statistics

Standard deviation1.000000463
Coefficient of variation (CV)-5.09439837 × 1015
Kurtosis0.2592189832
Mean-1.962941236 × 10-16
Median Absolute Deviation (MAD)0.4700726746
Skewness-0.869938033
Sum-2.200977178 × 10-10
Variance1.000000926
MonotonicityNot monotonic
2023-06-16T11:30:16.350932image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.57685612655668
 
0.5%
0.59798298835397
 
0.5%
0.64023671185309
 
0.5%
0.60326470375225
 
0.5%
0.5715744115217
 
0.5%
0.58213784195049
 
0.5%
0.619109854974
 
0.5%
0.54516583384878
 
0.5%
0.61382813464869
 
0.5%
0.56101098024851
 
0.4%
Other values (1382)1028128
95.2%
ValueCountFrequency (%)
-4.4460552611
< 0.1%
-4.435491831
< 0.1%
-4.4302101151
< 0.1%
-4.4249283991
< 0.1%
-4.4090832531
< 0.1%
-4.3932381071
< 0.1%
-4.3826746761
< 0.1%
-4.377392961
< 0.1%
-4.366829531
< 0.1%
-4.3615478141
< 0.1%
ValueCountFrequency (%)
3.0803892474
 
< 0.1%
3.0592623854
 
< 0.1%
3.03813552412
 
< 0.1%
3.03285380836
< 0.1%
3.02757209340
< 0.1%
3.02229037728
< 0.1%
3.0117269464
 
< 0.1%
2.9853183694
 
< 0.1%
2.9641915074
 
< 0.1%
2.9536280774
 
< 0.1%

VALUE_FUEL_QTY_LXT
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12510
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.739712397 × 10-17
Minimum-2.083012328
Maximum1.527027916
Zeros0
Zeros (%)0.0%
Negative513244
Negative (%)47.5%
Memory size8.2 MiB
2023-06-16T11:30:16.468747image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2.083012328
5-th percentile-1.744930106
Q1-0.7648650748
median0.0948461325
Q30.7833619239
95-th percentile1.457228443
Maximum1.527027916
Range3.610040245
Interquartile range (IQR)1.548226999

Descriptive statistics

Standard deviation1.000000463
Coefficient of variation (CV)1.483743525 × 1016
Kurtosis-0.9847081278
Mean6.739712397 × 10-17
Median Absolute Deviation (MAD)0.7465383988
Skewness-0.2418879082
Sum7.275957614 × 10-11
Variance1.000000926
MonotonicityNot monotonic
2023-06-16T11:30:16.581753image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.4595263681773
 
0.2%
1.4592391281565
 
0.1%
1.4615370531551
 
0.1%
1.4598136091519
 
0.1%
-1.4892859451494
 
0.1%
1.4606753311493
 
0.1%
1.4589518871429
 
0.1%
1.4609625711378
 
0.1%
-1.4889987041371
 
0.1%
1.460100851332
 
0.1%
Other values (12500)1064660
98.6%
ValueCountFrequency (%)
-2.083012328864
0.1%
-2.08272508853
 
< 0.1%
-2.08243784746
 
< 0.1%
-2.08215060766
 
< 0.1%
-2.08186336653
 
< 0.1%
-2.0815761258
 
< 0.1%
-2.08128888519
 
< 0.1%
-2.0810016445
 
< 0.1%
-2.0807144032
 
< 0.1%
-2.07008653
 
< 0.1%
ValueCountFrequency (%)
1.5270279161
 
< 0.1%
1.5267406761
 
< 0.1%
1.52645343512
 
< 0.1%
1.52616619530
< 0.1%
1.52587895447
< 0.1%
1.5255917132
 
< 0.1%
1.5253044731
 
< 0.1%
1.5247299911
 
< 0.1%
1.5244427512
 
< 0.1%
1.524155512
 
< 0.1%

VALUE_FUEL_QTY_RXT
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12613
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.021913719 × 10-17
Minimum-2.058404295
Maximum1.718758985
Zeros0
Zeros (%)0.0%
Negative521336
Negative (%)48.3%
Memory size8.2 MiB
2023-06-16T11:30:16.705021image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2.058404295
5-th percentile-1.746810556
Q1-0.7709501443
median0.08362336594
Q30.7881660115
95-th percentile1.462587738
Maximum1.718758985
Range3.77716328
Interquartile range (IQR)1.559116156

Descriptive statistics

Standard deviation1.000000463
Coefficient of variation (CV)-4.945811751 × 1016
Kurtosis-1.001204618
Mean-2.021913719 × 10-17
Median Absolute Deviation (MAD)0.7584734344
Skewness-0.2259314388
Sum-1.455191523 × 10-11
Variance1.000000926
MonotonicityNot monotonic
2023-06-16T11:30:16.820316image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.8555901051410
 
0.1%
-1.9872615521300
 
0.1%
-1.4984420621183
 
0.1%
-1.4987289281171
 
0.1%
1.479225961158
 
0.1%
1.4789390941106
 
0.1%
-1.6757251871077
 
0.1%
1.480660289995
 
0.1%
-1.041464686992
 
0.1%
1.479512826983
 
0.1%
Other values (12603)1068190
98.9%
ValueCountFrequency (%)
-2.058404295447
< 0.1%
-2.05811742925
 
< 0.1%
-2.05783056325
 
< 0.1%
-2.05754369728
 
< 0.1%
-2.05725683167
 
< 0.1%
-2.05696996531
 
< 0.1%
-2.05668309919
 
< 0.1%
-2.05639623322
 
< 0.1%
-2.0561093688
 
< 0.1%
-2.0558225021
 
< 0.1%
ValueCountFrequency (%)
1.7187589851
< 0.1%
1.7178983871
< 0.1%
1.7173246551
< 0.1%
1.7167509241
< 0.1%
1.7158903261
< 0.1%
1.7153165941
< 0.1%
1.7144559961
< 0.1%
1.7138822651
< 0.1%
1.7130216671
< 0.1%
1.7121610691
< 0.1%

FLIGHT_PHASE_COUNT
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.764335163 × 10-19
Minimum-1.221527046
Maximum1.660735031
Zeros0
Zeros (%)0.0%
Negative421624
Negative (%)39.1%
Memory size8.2 MiB
2023-06-16T11:30:16.922654image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1.221527046
5-th percentile-1.221527046
Q1-1.221527046
median0.6999810052
Q30.6999810052
95-th percentile1.660735031
Maximum1.660735031
Range2.882262076
Interquartile range (IQR)1.921508051

Descriptive statistics

Standard deviation1.000000463
Coefficient of variation (CV)3.617508023 × 1018
Kurtosis-1.595656437
Mean2.764335163 × 10-19
Median Absolute Deviation (MAD)0
Skewness-0.2772103602
Sum-2.431055357 × 10-11
Variance1.000000926
MonotonicityNot monotonic
2023-06-16T11:30:16.997931image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.6999810052593076
54.9%
-1.221527046420586
39.0%
1.66073503156961
 
5.3%
0.37972966344787
 
0.4%
1.0202323472539
 
0.2%
-0.9012757038615
 
0.1%
0.05947832162354
 
< 0.1%
-0.2607730202277
 
< 0.1%
1.340483689224
 
< 0.1%
-0.581024362146
 
< 0.1%
ValueCountFrequency (%)
-1.221527046420586
39.0%
-0.9012757038615
 
0.1%
-0.581024362146
 
< 0.1%
-0.2607730202277
 
< 0.1%
0.05947832162354
 
< 0.1%
0.37972966344787
 
0.4%
0.6999810052593076
54.9%
1.0202323472539
 
0.2%
1.340483689224
 
< 0.1%
1.66073503156961
 
5.3%
ValueCountFrequency (%)
1.66073503156961
 
5.3%
1.340483689224
 
< 0.1%
1.0202323472539
 
0.2%
0.6999810052593076
54.9%
0.37972966344787
 
0.4%
0.05947832162354
 
< 0.1%
-0.2607730202277
 
< 0.1%
-0.581024362146
 
< 0.1%
-0.9012757038615
 
0.1%
-1.221527046420586
39.0%

FUEL_USED_1
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct548360
Distinct (%)50.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-4.896822289 × 10-18
Minimum-0.8120907896
Maximum3.569960735
Zeros0
Zeros (%)0.0%
Negative694649
Negative (%)64.3%
Memory size8.2 MiB
2023-06-16T11:30:17.101027image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-0.8120907896
5-th percentile-0.8072445515
Q1-0.7969755366
median-0.427188757
Q30.5319078878
95-th percentile2.248153366
Maximum3.569960735
Range4.382051525
Interquartile range (IQR)1.328883424

Descriptive statistics

Standard deviation1.000000463
Coefficient of variation (CV)-2.042141626 × 1017
Kurtosis0.8416808337
Mean-4.896822289 × 10-18
Median Absolute Deviation (MAD)0.3767908673
Skewness1.320337837
Sum-1.364242053 × 10-12
Variance1.000000926
MonotonicityNot monotonic
2023-06-16T11:30:17.214031image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.74375942159008
 
0.8%
-0.81209078963720
 
0.3%
-0.72889329462845
 
0.3%
-0.76436313972464
 
0.2%
-0.76207613152048
 
0.2%
-0.76647654021863
 
0.2%
0.0097473394841704
 
0.2%
3.2595586461520
 
0.1%
1.5751919721380
 
0.1%
0.0036472562481128
 
0.1%
Other values (548350)1051885
97.4%
ValueCountFrequency (%)
-0.81209078963720
0.3%
-0.81209004094
 
< 0.1%
-0.81209003551
 
< 0.1%
-0.81209002971
 
< 0.1%
-0.81209002791
 
< 0.1%
-0.81209002621
 
< 0.1%
-0.81209001821
 
< 0.1%
-0.81209001311
 
< 0.1%
-0.81208999794
 
< 0.1%
-0.81208997941
 
< 0.1%
ValueCountFrequency (%)
3.5699607351
< 0.1%
3.5698515141
< 0.1%
3.5697273191
< 0.1%
3.5696142811
< 0.1%
3.5694868571
< 0.1%
3.5693840951
< 0.1%
3.5692455131
< 0.1%
3.5691148591
< 0.1%
3.5689607161
< 0.1%
3.5688174371
< 0.1%

TOTAL_FUEL_USED
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct594345
Distinct (%)55.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.307166781 × 10-16
Minimum-0.8348357577
Maximum3.707152325
Zeros0
Zeros (%)0.0%
Negative692732
Negative (%)64.2%
Memory size8.2 MiB
2023-06-16T11:30:17.335432image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-0.8348357577
5-th percentile-0.8314161509
Q1-0.8154989204
median-0.4222461457
Q30.5409583057
95-th percentile2.184922082
Maximum3.707152325
Range4.541988083
Interquartile range (IQR)1.356457226

Descriptive statistics

Standard deviation1.000000463
Coefficient of variation (CV)-4.334322388 × 1015
Kurtosis0.8201646648
Mean-2.307166781 × 10-16
Median Absolute Deviation (MAD)0.4058751075
Skewness1.296149382
Sum-2.482067885 × 10-10
Variance1.000000926
MonotonicityNot monotonic
2023-06-16T11:30:17.450418image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.1978620494432
 
< 0.1%
-0.6477205158340
 
< 0.1%
-0.1970402329324
 
< 0.1%
-0.23813688212
 
< 0.1%
-0.7413851688200
 
< 0.1%
-0.4938654068184
 
< 0.1%
0.5571965043176
 
< 0.1%
-0.4474741166154
 
< 0.1%
1.356413096138
 
< 0.1%
0.7391528636124
 
< 0.1%
Other values (594335)1077281
99.8%
ValueCountFrequency (%)
-0.834835757791
< 0.1%
-0.83483553171
 
< 0.1%
-0.83483552781
 
< 0.1%
-0.83483552781
 
< 0.1%
-0.83483552461
 
< 0.1%
-0.83483552351
 
< 0.1%
-0.83483552321
 
< 0.1%
-0.83483552271
 
< 0.1%
-0.83483552221
 
< 0.1%
-0.8348355221
 
< 0.1%
ValueCountFrequency (%)
3.7071523251
< 0.1%
3.7070323421
< 0.1%
3.7069074431
< 0.1%
3.7067903351
< 0.1%
3.7066689151
< 0.1%
3.7065477971
< 0.1%
3.7064085241
< 0.1%
3.7062687211
< 0.1%
3.7061218821
< 0.1%
3.7059738331
< 0.1%

VALUE_FOB_DIFF
Real number (ℝ)

Distinct181
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-3.117116984 × 10-17
Minimum-31.92252093
Maximum105.0665639
Zeros0
Zeros (%)0.0%
Negative594524
Negative (%)55.1%
Memory size8.2 MiB
2023-06-16T11:30:17.565346image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-31.92252093
5-th percentile-1.174552147
Q1-0.2567023323
median-0.02723987871
Q30.2022225749
95-th percentile1.120072389
Maximum105.0665639
Range136.9890848
Interquartile range (IQR)0.4589249072

Descriptive statistics

Standard deviation1.000000463
Coefficient of variation (CV)-3.208094109 × 1016
Kurtosis427.2265058
Mean-3.117116984 × 10-17
Median Absolute Deviation (MAD)0.2294624536
Skewness2.271480829
Sum5.10880227 × 10-12
Variance1.000000926
MonotonicityNot monotonic
2023-06-16T11:30:17.755111image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2022225749224396
20.8%
-0.02723987871216873
20.1%
-0.2567023323153134
14.2%
0.4316850285112308
10.4%
-0.486164785988486
 
8.2%
0.66114748250034
 
4.6%
-0.715627239547001
 
4.4%
0.890609935629074
 
2.7%
-0.945089693125527
 
2.4%
1.12007238918369
 
1.7%
Other values (171)114363
10.6%
ValueCountFrequency (%)
-31.922520931
 
< 0.1%
-30.316283751
 
< 0.1%
-28.480584124
< 0.1%
-28.251121674
< 0.1%
-27.562734311
 
< 0.1%
-25.038647324
< 0.1%
-22.973485244
< 0.1%
-22.514560338
< 0.1%
-22.055635421
 
< 0.1%
-21.596710524
< 0.1%
ValueCountFrequency (%)
105.06656391
 
< 0.1%
97.264840441
 
< 0.1%
94.281828551
 
< 0.1%
76.842682071
 
< 0.1%
72.941820361
 
< 0.1%
68.582033741
 
< 0.1%
64.681172031
 
< 0.1%
52.749124451
 
< 0.1%
39.210839681
 
< 0.1%
28.885029274
< 0.1%

TOTAL_FOB_BY_QTY
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct36486
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.519843761 × 10-17
Minimum-2.485447973
Maximum2.365564107
Zeros0
Zeros (%)0.0%
Negative537585
Negative (%)49.8%
Memory size8.2 MiB
2023-06-16T11:30:17.871454image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2.485447973
5-th percentile-1.592376738
Q1-0.7253787274
median0.006696673005
Q30.7785147559
95-th percentile1.679306703
Maximum2.365564107
Range4.85101208
Interquartile range (IQR)1.503893483

Descriptive statistics

Standard deviation1.000000463
Coefficient of variation (CV)1.050437894 × 1016
Kurtosis-0.750296096
Mean9.519843761 × 10-17
Median Absolute Deviation (MAD)0.7597940229
Skewness0.02385985756
Sum1.091393642 × 10-10
Variance1.000000926
MonotonicityNot monotonic
2023-06-16T11:30:17.985479image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.4700889482274
 
< 0.1%
-0.4698358101267
 
< 0.1%
0.1493399953249
 
< 0.1%
0.1528839288242
 
< 0.1%
-0.4771768152238
 
< 0.1%
0.7830712418233
 
< 0.1%
-0.4770502461228
 
< 0.1%
0.1489602882228
 
< 0.1%
-0.4702155173222
 
< 0.1%
-0.4735063126222
 
< 0.1%
Other values (36476)1077162
99.8%
ValueCountFrequency (%)
-2.4854479731
< 0.1%
-2.4848151281
< 0.1%
-2.4844354211
< 0.1%
-2.4839291441
< 0.1%
-2.4830431611
< 0.1%
-2.4820306091
< 0.1%
-2.4812711941
< 0.1%
-2.4807649181
< 0.1%
-2.4801320731
< 0.1%
-2.4796257971
< 0.1%
ValueCountFrequency (%)
2.3655641071
< 0.1%
2.3653109691
< 0.1%
2.3650578311
< 0.1%
2.3645515541
< 0.1%
2.3642984162
< 0.1%
2.3639187091
< 0.1%
2.363792141
< 0.1%
2.3636655711
< 0.1%
2.3632858641
< 0.1%
2.3630327261
< 0.1%

DELTA_VFOB_VS_VFOBQTY
Real number (ℝ)

Distinct64
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum-58.01236837
Maximum31.06189829
Zeros0
Zeros (%)0.0%
Negative362864
Negative (%)33.6%
Memory size8.2 MiB
2023-06-16T11:30:18.100048image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-58.01236837
5-th percentile-1.545645752
Q1-0.7503397996
median0.04496615277
Q30.8402721051
95-th percentile1.635578057
Maximum31.06189829
Range89.07426666
Interquartile range (IQR)1.590611905

Descriptive statistics

Standard deviation1.000000463
Coefficient of variation (CV)nan
Kurtosis49.21907661
Mean0
Median Absolute Deviation (MAD)0.7953059524
Skewness-1.298429938
Sum5.820766091 × 10-11
Variance1.000000926
MonotonicityNot monotonic
2023-06-16T11:30:18.210111image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.04496615277381579
35.3%
-0.7503397996265291
24.6%
0.8402721051255065
23.6%
-1.54564575281184
 
7.5%
1.63557805770919
 
6.6%
-2.3409517049450
 
0.9%
2.430884014934
 
0.5%
-3.1362576573217
 
0.3%
3.2261899621991
 
0.2%
-3.9315636091367
 
0.1%
Other values (54)4568
 
0.4%
ValueCountFrequency (%)
-58.012368371
 
< 0.1%
-52.44522671
 
< 0.1%
-39.720331471
 
< 0.1%
-36.539107662
 
< 0.1%
-35.74380171
 
< 0.1%
-33.357883851
 
< 0.1%
-31.767271946
< 0.1%
-29.381354081
 
< 0.1%
-26.995436232
 
< 0.1%
-26.200130281
 
< 0.1%
ValueCountFrequency (%)
31.061898291
 
< 0.1%
23.108838774
 
< 0.1%
21.518226871
 
< 0.1%
16.746391151
 
< 0.1%
15.95108521
 
< 0.1%
15.155779251
 
< 0.1%
14.36047337
< 0.1%
13.5651673412
< 0.1%
12.769861399
< 0.1%
11.974555449
< 0.1%

ALTITUDE_DIFF
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION

Distinct11598
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.381641041 × 10-16
Minimum-0.5823749053
Maximum4.987016407
Zeros0
Zeros (%)0.0%
Negative832834
Negative (%)77.1%
Memory size8.2 MiB
2023-06-16T11:30:18.323051image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-0.5823749053
5-th percentile-0.5823749053
Q1-0.5823749053
median-0.4395699998
Q3-0.01115528347
95-th percentile2.273723204
Maximum4.987016407
Range5.569391313
Interquartile range (IQR)0.5712196218

Descriptive statistics

Standard deviation1.000000463
Coefficient of variation (CV)7.237773294 × 1015
Kurtosis6.683303541
Mean1.381641041 × 10-16
Median Absolute Deviation (MAD)0.1428049055
Skewness2.573534831
Sum-3.183231456 × 10-11
Variance1.000000926
MonotonicityNot monotonic
2023-06-16T11:30:18.432441image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.5823749053320298
29.7%
-0.4395699998273671
25.4%
-0.2967650944109421
 
10.1%
-0.153960188967702
 
6.3%
-0.0111552834743390
 
4.0%
0.13164962228519
 
2.6%
0.274454527421176
 
2.0%
0.417259432916843
 
1.6%
0.560064338314255
 
1.3%
0.702869243813444
 
1.2%
Other values (11588)170846
15.8%
ValueCountFrequency (%)
-0.5823749053320298
29.7%
-0.58237347721
 
< 0.1%
-0.58237062111
 
< 0.1%
-0.58236919311
 
< 0.1%
-0.58236490891
 
< 0.1%
-0.58236348091
 
< 0.1%
-0.58236062486
 
< 0.1%
-0.58236062484
 
< 0.1%
-0.582360624813
 
< 0.1%
-0.58236062481
 
< 0.1%
ValueCountFrequency (%)
4.9870164071580
0.1%
4.8442115021516
0.1%
4.7014065971620
0.2%
4.5586016911905
0.2%
4.4157967861932
0.2%
4.272991881895
0.2%
4.1301869752133
0.2%
3.9873820692314
0.2%
3.8445771642348
0.2%
3.7017722582414
0.2%

VALUE_FOB_MISSING
Real number (ℝ)

HIGH CORRELATION

Distinct3691
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.695884959 × 10-17
Minimum-7.740997567
Maximum15.7259421
Zeros0
Zeros (%)0.0%
Negative707746
Negative (%)65.6%
Memory size8.2 MiB
2023-06-16T11:30:18.543671image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-7.740997567
5-th percentile-0.6790746681
Q1-0.4897860131
median-0.2665224713
Q30.2042723886
95-th percentile1.563267861
Maximum15.7259421
Range23.46693967
Interquartile range (IQR)0.6940584017

Descriptive statistics

Standard deviation1.000000463
Coefficient of variation (CV)-3.709358813 × 1016
Kurtosis46.42052649
Mean-2.695884959 × 10-17
Median Absolute Deviation (MAD)0.2717990944
Skewness4.848802632
Sum-2.705036195 × 10-11
Variance1.000000926
MonotonicityNot monotonic
2023-06-16T11:30:18.656856image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.572296452414577
 
1.4%
-0.557735786711771
 
1.1%
-0.562589341911424
 
1.1%
-0.552882231410913
 
1.0%
-0.567442897210737
 
1.0%
-0.548028676210479
 
1.0%
-0.538321565610318
 
1.0%
-0.543175120910029
 
0.9%
-0.52861445519994
 
0.9%
-0.53346801049812
 
0.9%
Other values (3681)969511
89.8%
ValueCountFrequency (%)
-7.7409975671
< 0.1%
-7.4983198041
< 0.1%
-7.4934662491
< 0.1%
-7.2459349311
< 0.1%
-6.9547216151
< 0.1%
-6.8042614021
< 0.1%
-6.6440940791
< 0.1%
-6.6004120811
< 0.1%
-6.386855651
< 0.1%
-6.2461025481
< 0.1%
ValueCountFrequency (%)
15.72594211
< 0.1%
15.711381431
< 0.1%
15.691967211
< 0.1%
15.667699441
< 0.1%
15.643431661
< 0.1%
15.599749661
< 0.1%
15.580335441
< 0.1%
15.551214111
< 0.1%
15.522092781
< 0.1%
15.502678561
< 0.1%

Interactions

2023-06-16T11:30:00.339440image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:26:52.747931image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:27:06.952606image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:27:17.168591image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:27:26.491758image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:27:36.451510image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:27:48.425894image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:27:59.421801image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:08.996007image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:17.978924image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:26.616165image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:35.977177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:45.208215image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:54.999371image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:04.409293image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:12.758024image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:21.406371image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:31.530267image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:41.480577image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:51.134623image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:30:00.758601image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:26:53.484366image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:27:07.390679image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:27:17.714918image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:27:26.952305image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:27:37.037525image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:27:48.810177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:27:59.929338image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:09.515047image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:18.405925image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:27.100074image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:36.408123image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:45.723678image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:55.511258image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:04.810724image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:13.177482image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:21.873253image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:32.022264image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:42.024997image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:51.554954image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:30:01.175146image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:26:54.336584image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:27:07.839709image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:27:18.194027image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:27:27.409264image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:27:37.545015image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:27:49.266018image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:00.490232image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:10.079237image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:18.826489image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:27.596504image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:36.840966image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:46.273739image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:56.057383image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:05.220193image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:13.591481image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:22.423705image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:32.526531image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:42.537187image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:51.968415image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:30:01.582463image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:26:55.245818image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:27:08.290649image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:27:18.661608image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:27:27.864874image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2023-06-16T11:27:55.444122image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:05.834108image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:14.947026image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:23.512504image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:32.788311image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:42.011193image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:51.366330image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:01.539787image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:09.850253image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:18.199670image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:27.838035image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:37.949439image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:47.732790image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:57.108937image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:30:06.232112image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:27:04.237670image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:27:14.187364image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:27:23.693093image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:27:33.365042image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:27:44.255442image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:27:55.913963image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:06.268346image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:15.364912image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:23.920719image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:33.217793image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:42.470967image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:51.792826image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:01.955045image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:10.256177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:18.600654image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:28.430034image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:38.455951image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:48.278090image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:57.564582image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:30:06.662790image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:27:04.724412image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:27:14.716782image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:27:24.168284image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:27:33.947545image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:27:45.001000image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:27:56.538923image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:06.712745image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:15.778280image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:24.339481image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:33.719647image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:42.926270image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:52.262192image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:02.378364image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:10.678528image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:19.026742image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:28.960953image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:38.947653image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:48.747416image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:58.042043image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:30:07.152724image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:27:05.186425image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:27:15.226071image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:27:24.625072image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:27:34.513984image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:27:45.751677image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:27:57.290580image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:07.155747image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:16.276560image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:24.757478image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:34.190844image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:43.377174image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:52.735190image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:02.801711image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:11.099575image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:19.504778image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:29.515918image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:39.436867image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:49.403876image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:58.508791image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:30:07.581545image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:27:05.645238image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:27:15.700695image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:27:25.129455image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:27:34.996924image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:27:46.648499image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:27:57.929401image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:07.590747image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:16.694775image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:25.209554image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:34.648763image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:43.830082image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:53.260299image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:03.208055image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:11.511930image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:19.958552image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:30.037435image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:39.999490image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:49.850490image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:58.953372image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:30:08.087630image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:27:06.094624image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:27:16.210182image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:27:25.606205image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:27:35.465126image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:27:47.318631image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:27:58.465581image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:08.024003image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:17.118430image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:25.677251image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:35.104787image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:44.301626image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:53.817307image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:03.612194image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:11.934172image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:20.510431image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:30.548433image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:40.497072image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:50.286783image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:59.428619image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:30:08.498803image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:27:06.513562image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:27:16.686643image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:27:26.068447image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:27:35.934501image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:27:47.941000image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:27:58.932083image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:08.469414image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:17.534379image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:26.110757image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:35.543287image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:44.754422image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:28:54.371737image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:04.007016image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:12.351170image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:20.954376image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:31.049178image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:40.982712image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:50.716535image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-16T11:29:59.901190image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2023-06-16T11:30:18.780115image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-06-16T11:30:19.023152image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-06-16T11:30:19.301085image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-06-16T11:30:19.578506image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2023-06-16T11:30:08.821722image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-16T11:30:09.848445image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

FUEL_USED_2FUEL_USED_3FUEL_USED_4FW_GEO_ALTITUDEVALUE_FOBVALUE_FUEL_QTY_CTVALUE_FUEL_QTY_FT1VALUE_FUEL_QTY_FT2VALUE_FUEL_QTY_FT3VALUE_FUEL_QTY_FT4VALUE_FUEL_QTY_LXTVALUE_FUEL_QTY_RXTFLIGHT_PHASE_COUNTFUEL_USED_1TOTAL_FUEL_USEDVALUE_FOB_DIFFTOTAL_FOB_BY_QTYDELTA_VFOB_VS_VFOBQTYALTITUDE_DIFFVALUE_FOB_MISSING
0-0.800963-0.804636-0.819411-0.963745-0.707146-0.4109970.8403040.3494730.6369250.656082-0.765440-0.814841-1.221527-0.808232-0.833425-0.027240-0.7071530.044966-0.581304-0.572296
1-0.800903-0.804636-0.819411-0.963757-0.707273-0.4109970.8403040.3494730.6369250.656082-0.765727-0.814841-1.221527-0.808171-0.833394-0.027240-0.7072790.044966-0.556899-0.567443
2-0.800843-0.804635-0.819411-0.963757-0.707146-0.4109970.8403040.3494730.6369250.656082-0.765727-0.814554-1.221527-0.808110-0.8333630.431685-0.7071530.044966-0.580733-0.572296
3-0.800782-0.804635-0.819411-0.963756-0.707146-0.4109970.8348450.3453470.6369250.656082-0.765727-0.814554-1.221527-0.808049-0.8333320.202223-0.7074061.635578-0.582232-0.572296
4-0.800721-0.804636-0.819411-0.963756-0.707146-0.4109970.8348450.3453470.6369250.656082-0.765727-0.814267-1.221527-0.807988-0.8333010.202223-0.7072790.840272-0.580990-0.572296
5-0.800661-0.804636-0.819411-0.963739-0.707526-0.4109970.8348450.3453470.6369250.656082-0.765727-0.814554-1.221527-0.807927-0.833270-0.486165-0.707406-0.750340-0.547159-0.557736
6-0.800601-0.804626-0.819411-0.963739-0.707399-0.4109970.8348450.3453470.6369250.656082-0.765727-0.814554-1.221527-0.807866-0.8332360.431685-0.7074060.044966-0.581547-0.562589
7-0.800541-0.804564-0.819411-0.963738-0.707652-0.4109970.8348450.3453470.6369250.656082-0.765727-0.814841-1.221527-0.807805-0.833189-0.256702-0.707532-0.750340-0.581647-0.552882
8-0.800476-0.804500-0.819411-0.963738-0.707906-0.4109970.8348450.3453470.6329790.656082-0.766014-0.814841-1.221527-0.807744-0.833141-0.256702-0.707786-0.750340-0.581147-0.543175
9-0.800416-0.804437-0.819411-0.963754-0.707779-0.4109970.8348450.3453470.6329790.656082-0.766014-0.814554-1.221527-0.807677-0.8330930.431685-0.707659-0.750340-0.547973-0.548029

Last rows

FUEL_USED_2FUEL_USED_3FUEL_USED_4FW_GEO_ALTITUDEVALUE_FOBVALUE_FUEL_QTY_CTVALUE_FUEL_QTY_FT1VALUE_FUEL_QTY_FT2VALUE_FUEL_QTY_FT3VALUE_FUEL_QTY_FT4VALUE_FUEL_QTY_LXTVALUE_FUEL_QTY_RXTFLIGHT_PHASE_COUNTFUEL_USED_1TOTAL_FUEL_USEDVALUE_FOB_DIFFTOTAL_FOB_BY_QTYDELTA_VFOB_VS_VFOBQTYALTITUDE_DIFFVALUE_FOB_MISSING
1079555-0.794285-0.798157-0.814812-0.9293251.8728392.7906460.6000850.5763570.0962640.5451661.4560791.502175-1.221527-0.802346-0.8273451.3495351.872957-0.750340-0.439570-0.392715
1079556-0.794211-0.798093-0.814749-0.9293251.8732192.7906460.6000850.5763570.0962640.5451661.4566541.502462-1.221527-0.802284-0.8272770.8906101.873337-0.750340-0.582375-0.407276
1079557-0.794144-0.798029-0.814685-0.9292581.8737252.7913240.6055440.5763570.0962640.5451661.4572281.502462-1.221527-0.802221-0.8272111.1200721.873843-0.750340-0.439570-0.426690
1079558-0.794070-0.797965-0.814622-0.9292581.8739782.7920020.5946250.5804820.0962640.5398841.4578031.502462-1.221527-0.802159-0.8271430.6611471.8739700.044966-0.582375-0.436397
1079559-0.794005-0.797892-0.814551-0.9293251.8741052.7926800.6000850.5804820.0923170.5451661.4578031.502175-1.221527-0.802089-0.8270710.4316851.8740960.044966-0.439570-0.441250
1079560-0.793938-0.797828-0.814488-0.9293251.8733462.7913240.5946250.5804820.0962640.5398841.4575161.501888-1.221527-0.802026-0.827005-1.1745521.873464-0.750340-0.582375-0.412129
1079561-0.793872-0.797756-0.814417-0.9293931.8724602.7892890.5946250.5763570.0962640.5398841.4563671.502175-1.221527-0.801956-0.826933-1.4040151.872578-0.750340-0.439570-0.378154
1079562-0.793798-0.797700-0.814362-0.9293931.8715742.7892890.5891660.5722320.1002100.5346021.4552181.502175-1.221527-0.801894-0.826869-1.4040151.871818-1.545646-0.582375-0.344179
1079563-0.793732-0.797628-0.814291-0.9293251.8715742.7899680.6000850.5681070.1002100.5293211.4552181.501888-1.221527-0.801831-0.8267990.2022231.871818-1.545646-0.439570-0.344179
1079564-0.793724-0.797620-0.814283-0.9292581.8708142.7892890.5727870.5722320.0962640.5293211.4557921.501315-1.221527-0.801823-0.826791-1.1745521.871059-1.545646-0.439570-0.315058

Duplicate rows

Most frequently occurring

FUEL_USED_2FUEL_USED_3FUEL_USED_4FW_GEO_ALTITUDEVALUE_FOBVALUE_FUEL_QTY_CTVALUE_FUEL_QTY_FT1VALUE_FUEL_QTY_FT2VALUE_FUEL_QTY_FT3VALUE_FUEL_QTY_FT4VALUE_FUEL_QTY_LXTVALUE_FUEL_QTY_RXTFLIGHT_PHASE_COUNTFUEL_USED_1TOTAL_FUEL_USEDVALUE_FOB_DIFFTOTAL_FOB_BY_QTYDELTA_VFOB_VS_VFOBQTYALTITUDE_DIFFVALUE_FOB_MISSING# duplicates
273-0.8026060.027018-0.015179-0.975731-1.250508-0.4109971.0641450.931122-0.1405220.920168-1.408571-1.414390-1.2215270.009747-0.1978620.202223-1.2506400.840272-0.582375-0.57229616
46956-0.777668-0.804638-0.806606-0.999204-0.517039-0.4109970.2834320.5804820.6606040.381433-0.523870-0.598831-1.221527-0.783661-0.8178080.202223-0.5170460.044966-0.582375-0.57229616
67-0.802606-0.787053-0.809541-0.9794520.150360-0.4109970.5181920.9393720.6882290.8567870.1655070.160503-1.221527-0.762076-0.8148450.2022230.150479-0.750340-0.582375-0.56744312
170-0.802606-0.135830-0.176379-0.925199-1.183046-0.4109970.4635960.9228720.2186031.506438-1.401390-1.293620-1.221527-0.812090-0.4938650.202223-1.182799-1.545646-0.582375-0.57715012
202-0.8026060.027018-0.015179-0.984254-1.246331-0.4109970.9385761.0920040.0449600.936013-1.414029-1.418120-1.2215270.009747-0.1978620.202223-1.2463370.044966-0.582375-0.58685712
205-0.8026060.027018-0.015179-0.984254-1.246331-0.4109970.9440361.0878790.0449600.936013-1.414029-1.418120-1.2215270.009747-0.1978620.202223-1.2463370.044966-0.582375-0.58685712
47567-0.777668-0.786688-0.819411-0.998798-0.498813-0.4109970.5400300.5804820.9921040.455377-0.524157-0.599117-1.221527-0.797321-0.8201270.202223-0.4989470.840272-0.582375-0.56744312
52367-0.763117-0.804638-0.196163-0.9908160.152511-0.410997-0.1096550.584607-0.2510220.0909380.2289880.268939-1.221527-0.764363-0.6477210.2022230.1523780.840272-0.582375-0.55773612
72-0.802606-0.787053-0.809541-0.9794520.150486-0.4109970.5181920.9434970.6882290.8567870.1655070.160217-1.221527-0.762076-0.8148450.2022230.1504790.044966-0.439570-0.5722968
93-0.802606-0.746533-0.762750-0.9797900.082645-0.410997-0.0168430.588733-0.3891470.3708690.1680920.161651-1.221527-0.755679-0.7904670.2022230.082765-0.750340-0.582375-0.5722968